Evaluation of efficiency of different estimation methods for missing climatological data
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Station-based observational datasets often contain missing values due to factors such as observer absence, instrument failures, and interrupted communication.
“These SCDs are useful because station-based observational datasets often contain missing values due to factors such as observer absence, instrument failures, and interrupted communication (Hasanpour Kashani and Dinpashoh, 2012).”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Hasanpour Kashani and Dinpashoh, 2012
Cited by
Sorted by citations
Al-Mukhtar, M., & Al-Yaseen, F. (2019) · Hydrology · doi:10.3390/hydrology6010024 · Cited by 96 · see citing works
SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., & Whitfield, P. H. (2020) · Earth System Science Data · doi:10.5194/essd-12-2381-2020 · Cited by 82 · see citing works
- Authors
- Hasanpour Kashani, M., & Dinpashoh, Y.
- Affiliations
- University of Tabriz (IR)
- ORCID
- Dinpashoh, Yagob
- Venue
- Stochastic Environmental Research and Risk Assessment
- Published
- 2011-11-04
- Volume
- 26
- Issue
- 1
- Pages
- 59-71
- ISSN
- 1436-3240 (ISSN-L)
- 1436-3259
- OpenAlex
- W2080352926
- Cited by
- 107
- References
- 0
- Citations by year
- 2026: 9 · 2025: 6 · 2024: 9 · 2023: 4 · 2022: 14 · 2021: 17 · 2020: 9 · 2019: 14 · 2018: 4 · 2017: 6 · 2016: 6 · 2015: 2 · 2014: 5 · 2013: 2
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Stochastic Environmental Research and Risk Assessment (publishedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Earth and Planetary Sciences › Geology › Environmental Monitoring and Data Management
- Keywords
- missing data estimation, climate data, artificial neural networks, genetic programming, multiple linear regression, kriging, precipitation, temperature, relative humidity, wind speed, arid and semi-arid regions, Iran
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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